Multi-objective optimization of injection molding energy consumption and product quality using a combined approach of RSM and BP neural network

Abstract

With the global environmental challenges and the urgent energy-saving needs of the polymer material processing industry, an innovative multi-objective optimization method of energy consumption and product quality for injection molding process parameters was proposed. The influence mechanism of seven key process parameters on the synergistic optimization of energy consumption and quality was systematically investigated by constructing a dedicated experimental platform. The hybrid assignment strategy integrating grey correlation analysis and entropy weight method was adopted to quantitatively evaluate the three major indexes of injection molding energy consumption, product quality and tensile strength. The mathematical model was established by using the response surface method (RSM) and BP neural network combined with genetic algorithm respectively, and the optimization was computed by using the Design-Expert and MATLAB software, so as to realize the multi-objective optimization of product quality and energy consumption. The results showed that both optimization methods obtained good results, in which the response surface method optimizes the injection molding process with energy consumption decreased by 6.73%, product tensile strength increased by 7.06%, and the comprehensive optimization rate was 13.81%. The BP neural network-genetic algorithm optimized the injection molding process with energy consumption decreased by 9.32%, product tensile strength enhanced by 4.81%, and the comprehensive optimization rate was 14.15%.